Regular and Static Sector-Based Cell Switch-Off Patterns
Bibliographic record
Abstract
Energy saving in cellular networks can be achieved by implementing the cell switch-off (CSO) approach in periods of light traffic. Regular static CSO (CSO patterns) is a type of CSO where the set of active cells is predetermined such that they are located on a regular grid. It is known that regular cell layouts generally provide the best coverage and downlink SINR. Furthermore, CSO patterns assure that interfering cells are as far away as possible and help in modeling interference accurately. Existing lit- erature on CSO patterns focuses only on site-level CSO (switching off entire BSs); however, significant gains can sometimes be obtained from sector-level CSO patterns (switching off individual sectors). This paper is the first to introduce and investi- gate sector- based regular CSO patterns by providing illustrative examples. We compare the performances of different CSO patterns in terms of the number of supported users. Also, we analytically compare site- based versus sector-based CSO patterns in terms of power saving. Our results show that patterns with only one of the three sectors active (each with the same orientation) can support the most users per sector, due to a favourable interference situation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".